pyiblm 2.0.2

Interpretable Boosted Linear Models: GLM + XGBoost ensemble with SHAP-based interpretability

pyIBLM

Interpretable Boosted Linear Models

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Overview

pyIBLM implements Interpretable Boosted Linear Models — a hybrid modelling approach that combines the transparency of Generalized Linear Models (GLMs) with the predictive power of gradient boosting.

The model is a two-stage ensemble:

1. A GLM is fitted on the training data, producing interpretable coefficient estimates.
2. An XGBoost booster is trained on the GLM residuals, using the GLM's linear predictor as its base margin — learning only what the GLM could not capture.

Depending on the link function, the two components are combined as:

• Multiplicative (log-link families: Poisson, Gamma, Tweedie): prediction = GLM prediction × Booster correction
• Additive (identity-link families: Gaussian): prediction = GLM prediction + Booster correction

SHAP values decompose the booster correction back onto the original GLM feature scale, making the full model auditable and interpretable at the individual prediction level.

The package provides:

• Fitting of IBLM models across Poisson, Quasi-Poisson, Gamma, Tweedie, and Gaussian families
• SHAP-based explainability tools with beta coefficient visualisations
• Model comparison via pinball scores and correction corridor plots
• Bundled insurance pricing datasets (freMTPLmini, freMTPL2freq)

An equivalent R package is available on CRAN: 🔗 https://CRAN.R-project.org/package=IBLM

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Installation

Install the released version from PyPI:

pip install pyiblm

To use load_freMTPL2freq() (downloads the full French MTPL dataset), install with the optional data dependency:

pip install "pyiblm[data]"

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Quick start

import numpy as np
from iblm import (
    load_freMTPLmini,
    split_into_train_validate_test,
    IBLM,
    ExplainIBLM,
    get_pinball_scores,
)

# Load and prepare data
df = load_freMTPLmini()
df["LogExposure"] = np.log(df["Exposure"])
df = df.drop(columns=["Exposure"])

df_dict = split_into_train_validate_test(df, seed=9000)

# Fit model
model = IBLM()
model.fit(
    df_dict,
    response_var="ClaimNb",
    offset_var="LogExposure",
    family="poisson",
)

# Evaluate
scores = get_pinball_scores(df_dict["test"], model)
print(scores)

# Explain
ex = ExplainIBLM(model, df_dict["test"])
fig = ex.beta_corrected_scatter("DrivAge", color="VehPower")
fig.show()

Documentation

For full documentation on the R implementation (functions, methods and theoretical background):

🔗 https://ifoa-adswp.github.io/IBLM/

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Contributing

Contributions are welcome. To report a bug or suggest a feature, please open an issue on GitHub:

🔗 https://github.com/IFoA-ADSWP/pyIBLM/issues

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Citation

If you use pyIBLM in research or teaching, please cite it as:

Gawlowski, K. and Beard, P. (2026). pyIBLM: Interpretable Boosted Linear Models. Python package version 2.0.1.

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Authors

• Karol Gawlowski — [e-mail]
• Paul Beard — [e-mail] Additional contributions by Zhouwen Zhou.

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License

This package is licensed under the MIT License. See the LICENSE file for full details.